Using Cuckoo Search Algorithm to Predict Corporate Financial Risks and Alleviate Economic Uncertainty

Predicting financial risk accurately is crucial for maintaining economic sustainability and investor trust in an era of increasing economic volatility and business instability. There are several problems with traditional financial risk assessment models, including their inflexibility when confronted with high-dimensional, non-linear data and their inability to dynamically adjust to changing financial situations. Advanced forecasting models must be combined with robust optimization methods to address these challenges effectively. Conventional methods, such as logistic regression, decision trees, and linear discriminant analysis, often struggle to accurately detect early financial risks because they are deterministic and unsuited for exploring global optimal solutions. Problems like this contribute to macroeconomic uncertainty by delaying the identification of potential defaults. This research presents a new hybrid framework, the Financial Risk Prediction Framework utilizing Cuckoo Search Optimization (FRPF-CSO), to enhance prediction accuracy and optimize feature selection in high-dimensional financial datasets. This framework combines a Backpropagation Neural Network (BPNN) with the Cuckoo Search Algorithm (CSA) to build an adaptive learning system. CSA is used to tune the network weights and feature parameters worldwide to improve the neural model’s convergence speed and predictive ability. The system’s dynamic learning capabilities identify patterns connected to company failures and financial decline. Various real-world corporate finance datasets have been used for experimental validation. The experimental results demonstrate that the proposed FRPF-CSO model achieves a high early warning lead time of 2.1 quarters, a prediction accuracy of 95.72%, a convergence rate of 9.87 s, a risk detection ratio of 97.63%, and a computational efficiency ratio of 98.2% compared to other existing methods.